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Phase transitions in project scheduling

Author

Listed:
  • W Herroelen

    (Katholieke Universiteit Leuven)

  • B De Reyck

    (Erasmus University Rotterdam)

Abstract

Researchers in the area of artificial intelligence have recently shown that many NP-complete problems exhibit phase transitions. Often, problem instances change from being easy to being hard to solve to again being easy to solve when certain of their characteristics are modified. Most often the transitions are sharp, but sometimes they are rather continuous in the order parameters that are characteristic of the system as a whole. To the best of our knowledge, no evidence has been provided so far that similar phase transitions occur in NP-hard scheduling problems. In this paper we report on the existence of phase transitions in various resource-constrained project scheduling problems. We discuss the use of network complexity measures and resource parameters as potential order parameters. We show that while the network complexity measures seem to reveal continuous easy-hard or hard-easy phase transitions, the resource parameters exhibit a relatively sharp easy-hard-easy transition behaviour.

Suggested Citation

  • W Herroelen & B De Reyck, 1999. "Phase transitions in project scheduling," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(2), pages 148-156, February.
    • Handle: RePEc:pal:jorsoc:v:50:y:1999:i:2:d:10.1057_palgrave.jors.2600680
    DOI: 10.1057/palgrave.jors.2600680
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    Citations

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    Cited by:

    1. V. Van Peteghem & M. Vanhoucke, 2009. "Using Resource Scarceness Characteristics to Solve the Multi-Mode Resource-Constrained Project Scheduling Problem," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 09/595, Ghent University, Faculty of Economics and Business Administration.
    2. D. Debels & M. Vanhoucke, 2006. "The impact of various activity assumptions on the lead-time and resource utilization of resource-constrained projects," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 06/385, Ghent University, Faculty of Economics and Business Administration.
    3. Vanhoucke, Mario, 2005. "New computational results for the discrete time/cost trade-off problem with time-switch constraints," European Journal of Operational Research, Elsevier, vol. 165(2), pages 359-374, September.
    4. M. Vanhoucke & J. Coelho & L. V. Tavares & D. Debels, 2004. "On The Morphological Structure Of A Network," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/272, Ghent University, Faculty of Economics and Business Administration.
    5. M Vanhoucke & S Vandevoorde, 2007. "A simulation and evaluation of earned value metrics to forecast the project duration," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(10), pages 1361-1374, October.
    6. Mario Vanhoucke, 2002. "Optimal due date assignment in project scheduling," Vlerick Leuven Gent Management School Working Paper Series 2002-19, Vlerick Leuven Gent Management School.
    7. Debels, D. & Vanhoucke, M., 2006. "Meta-Heuristic resource constrained project scheduling: solution space restrictions and neighbourhood extensions," Vlerick Leuven Gent Management School Working Paper Series 2006-18, Vlerick Leuven Gent Management School.
    8. Vanhoucke, Mario & Coelho, Jose & Debels, Dieter & Maenhout, Broos & Tavares, Luis V., 2008. "An evaluation of the adequacy of project network generators with systematically sampled networks," European Journal of Operational Research, Elsevier, vol. 187(2), pages 511-524, June.
    9. Vanhoucke, M., 2006. "A scatter search procedure for maximizing the net present value of a project under renewable resource constraints," Vlerick Leuven Gent Management School Working Paper Series 2006-40, Vlerick Leuven Gent Management School.
    10. Šůcha, Přemysl & Agnetis, Alessandro & Šidlovský, Marko & Briand, Cyril, 2021. "Nash equilibrium solutions in multi-agent project scheduling with milestones," European Journal of Operational Research, Elsevier, vol. 294(1), pages 29-41.
    11. Dayal Madhukar & Verma, Sanjay, 2015. "Multi-processor Exact Procedures for Regular Measures of the Multi-mode RCPSP," IIMA Working Papers WP2015-03-25, Indian Institute of Management Ahmedabad, Research and Publication Department.
    12. Alessandro Agnetis & Cyril Briand & Sandra Ulrich Ngueveu & Přemysl Šůcha, 2020. "Price of anarchy and price of stability in multi-agent project scheduling," Annals of Operations Research, Springer, vol. 285(1), pages 97-119, February.
    13. Messelis, Tommy & De Causmaecker, Patrick, 2014. "An automatic algorithm selection approach for the multi-mode resource-constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 233(3), pages 511-528.
    14. Servranckx, Tom & Vanhoucke, Mario, 2019. "A tabu search procedure for the resource-constrained project scheduling problem with alternative subgraphs," European Journal of Operational Research, Elsevier, vol. 273(3), pages 841-860.
    15. Hongbo Li & Erik Demeulemeester, 2016. "A genetic algorithm for the robust resource leveling problem," Journal of Scheduling, Springer, vol. 19(1), pages 43-60, February.

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